Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells7600
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 5 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 3 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
WSPM is highly overall correlated with NO2High correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 750 (2.1%) missing valuesMissing
PM10 has 553 (1.6%) missing valuesMissing
SO2 has 663 (1.9%) missing valuesMissing
NO2 has 1601 (4.6%) missing valuesMissing
CO has 3197 (9.1%) missing valuesMissing
O3 has 664 (1.9%) missing valuesMissing
RAIN is highly skewed (γ1 = 27.3358138)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33673 (96.0%) zerosZeros
WSPM has 623 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-08 05:09:26.670019
Analysis finished2024-03-08 05:10:06.679224
Duration40.01 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:07.307926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:10:07.640200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:10:07.889102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:10:08.089565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:08.288498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:10:08.481032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:08.640373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:10:08.900100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:09.121259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:10:09.300546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct582
Distinct (%)1.7%
Missing750
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean86.194297
Minimum3
Maximum737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:09.461645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q122
median61
Q3119
95-th percentile260
Maximum737
Range734
Interquartile range (IQR)97

Descriptive statistics

Standard deviation86.575127
Coefficient of variation (CV)1.0044183
Kurtosis5.6455218
Mean86.194297
Median Absolute Deviation (MAD)44
Skewness1.9792416
Sum2957671.1
Variance7495.2526
MonotonicityNot monotonic
2024-03-08T12:10:09.660264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 892
 
2.5%
10 545
 
1.6%
8 524
 
1.5%
12 517
 
1.5%
9 504
 
1.4%
11 504
 
1.4%
13 490
 
1.4%
7 443
 
1.3%
14 431
 
1.2%
15 418
 
1.2%
Other values (572) 29046
82.8%
(Missing) 750
 
2.1%
ValueCountFrequency (%)
3 892
2.5%
4 283
 
0.8%
4.3 1
 
< 0.1%
4.4 1
 
< 0.1%
5 340
 
1.0%
6 402
1.1%
7 443
1.3%
8 524
1.5%
9 504
1.4%
9.6 1
 
< 0.1%
ValueCountFrequency (%)
737 1
< 0.1%
695 1
< 0.1%
685 2
< 0.1%
684 1
< 0.1%
681 1
< 0.1%
680 1
< 0.1%
679 1
< 0.1%
678 1
< 0.1%
671 1
< 0.1%
670 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct666
Distinct (%)1.9%
Missing553
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean110.33674
Minimum2
Maximum955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:09.965700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q138
median86
Q3151
95-th percentile296.5
Maximum955
Range953
Interquartile range (IQR)113

Descriptive statistics

Standard deviation98.21986
Coefficient of variation (CV)0.89018271
Kurtosis6.1505909
Mean110.33674
Median Absolute Deviation (MAD)54
Skewness1.9422459
Sum3807831.3
Variance9647.1409
MonotonicityNot monotonic
2024-03-08T12:10:10.273207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 405
 
1.2%
5 377
 
1.1%
16 296
 
0.8%
22 291
 
0.8%
24 286
 
0.8%
21 283
 
0.8%
18 281
 
0.8%
19 280
 
0.8%
14 279
 
0.8%
13 276
 
0.8%
Other values (656) 31457
89.7%
(Missing) 553
 
1.6%
ValueCountFrequency (%)
2 8
 
< 0.1%
3 86
 
0.2%
4 25
 
0.1%
5 377
1.1%
6 405
1.2%
7 170
0.5%
8 204
0.6%
9 203
0.6%
10 251
0.7%
11 233
0.7%
ValueCountFrequency (%)
955 1
< 0.1%
915 1
< 0.1%
907 1
< 0.1%
891 1
< 0.1%
888 1
< 0.1%
876 1
< 0.1%
857 1
< 0.1%
856 1
< 0.1%
848 1
< 0.1%
847 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct392
Distinct (%)1.1%
Missing663
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean18.531107
Minimum0.2856
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:10.505513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q14
median10
Q324
95-th percentile66
Maximum300
Range299.7144
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.905655
Coefficient of variation (CV)1.2360651
Kurtosis9.4647721
Mean18.531107
Median Absolute Deviation (MAD)8
Skewness2.5821137
Sum637488.6
Variance524.66902
MonotonicityNot monotonic
2024-03-08T12:10:10.800580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6248
 
17.8%
3 1788
 
5.1%
4 1671
 
4.8%
5 1432
 
4.1%
6 1408
 
4.0%
7 1257
 
3.6%
8 1108
 
3.2%
9 953
 
2.7%
10 920
 
2.6%
11 819
 
2.3%
Other values (382) 16797
47.9%
ValueCountFrequency (%)
0.2856 14
 
< 0.1%
0.5712 8
 
< 0.1%
0.8568 15
 
< 0.1%
1 269
 
0.8%
1.1424 11
 
< 0.1%
1.428 11
 
< 0.1%
1.7136 11
 
< 0.1%
1.9992 27
 
0.1%
2 6248
17.8%
2.1 1
 
< 0.1%
ValueCountFrequency (%)
300 1
< 0.1%
278 1
< 0.1%
230 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
198 1
< 0.1%
196 2
< 0.1%
192 1
< 0.1%
188 1
< 0.1%
187 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct701
Distinct (%)2.1%
Missing1601
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean53.699443
Minimum2
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:11.017999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q127
median47
Q373
95-th percentile118
Maximum258
Range256
Interquartile range (IQR)46

Descriptive statistics

Standard deviation33.95923
Coefficient of variation (CV)0.63239445
Kurtosis1.2315764
Mean53.699443
Median Absolute Deviation (MAD)22
Skewness1.0279943
Sum1796944.5
Variance1153.2293
MonotonicityNot monotonic
2024-03-08T12:10:11.242227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 468
 
1.3%
32 456
 
1.3%
30 448
 
1.3%
34 444
 
1.3%
28 436
 
1.2%
36 433
 
1.2%
23 430
 
1.2%
42 427
 
1.2%
26 425
 
1.2%
37 425
 
1.2%
Other values (691) 29071
82.9%
(Missing) 1601
 
4.6%
ValueCountFrequency (%)
2 55
0.2%
3 9
 
< 0.1%
4 25
 
0.1%
5 56
0.2%
5.5431 1
 
< 0.1%
5.7484 1
 
< 0.1%
6 101
0.3%
6.5696 1
 
< 0.1%
6.7749 1
 
< 0.1%
6.9802 4
 
< 0.1%
ValueCountFrequency (%)
258 1
< 0.1%
257 1
< 0.1%
256 1
< 0.1%
254 1
< 0.1%
249 1
< 0.1%
247 1
< 0.1%
244 1
< 0.1%
241 1
< 0.1%
236 2
< 0.1%
232 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)0.4%
Missing3197
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean1330.0691
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:11.539029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1600
median1000
Q31700
95-th percentile3600
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1191.3059
Coefficient of variation (CV)0.89567216
Kurtosis8.6520305
Mean1330.0691
Median Absolute Deviation (MAD)500
Skewness2.4522127
Sum42385313
Variance1419209.7
MonotonicityNot monotonic
2024-03-08T12:10:11.871273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 2419
 
6.9%
400 2137
 
6.1%
700 2076
 
5.9%
600 2050
 
5.8%
500 1978
 
5.6%
800 1855
 
5.3%
900 1728
 
4.9%
1000 1566
 
4.5%
1100 1507
 
4.3%
1200 1335
 
3.8%
Other values (105) 13216
37.7%
(Missing) 3197
 
9.1%
ValueCountFrequency (%)
100 277
 
0.8%
200 974
2.8%
300 2419
6.9%
400 2137
6.1%
500 1978
5.6%
600 2050
5.8%
700 2076
5.9%
800 1855
5.3%
900 1728
4.9%
1000 1566
4.5%
ValueCountFrequency (%)
10000 10
< 0.1%
9900 2
 
< 0.1%
9800 1
 
< 0.1%
9600 1
 
< 0.1%
9500 4
 
< 0.1%
9400 5
< 0.1%
9300 5
< 0.1%
9200 5
< 0.1%
9100 5
< 0.1%
9000 3
 
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct737
Distinct (%)2.1%
Missing664
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean57.210637
Minimum0.6426
Maximum1071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:12.089399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.6426
5-th percentile2
Q112
median44.1252
Q381
95-th percentile173
Maximum1071
Range1070.3574
Interquartile range (IQR)69

Descriptive statistics

Standard deviation58.033275
Coefficient of variation (CV)1.0143791
Kurtosis43.260873
Mean57.210637
Median Absolute Deviation (MAD)33.8748
Skewness3.5145175
Sum1968045.9
Variance3367.861
MonotonicityNot monotonic
2024-03-08T12:10:12.340250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2950
 
8.4%
3 904
 
2.6%
4 862
 
2.5%
5 600
 
1.7%
6 541
 
1.5%
8 476
 
1.4%
7 466
 
1.3%
10 388
 
1.1%
9 377
 
1.1%
11 345
 
1.0%
Other values (727) 26491
75.6%
(Missing) 664
 
1.9%
ValueCountFrequency (%)
0.6426 1
 
< 0.1%
0.8568 1
 
< 0.1%
1 94
 
0.3%
1.071 1
 
< 0.1%
1.2852 1
 
< 0.1%
1.4994 1
 
< 0.1%
1.7136 1
 
< 0.1%
2 2950
8.4%
2.142 3
 
< 0.1%
2.3562 3
 
< 0.1%
ValueCountFrequency (%)
1071 14
< 0.1%
1050 1
 
< 0.1%
1026 1
 
< 0.1%
500 1
 
< 0.1%
413 1
 
< 0.1%
346 1
 
< 0.1%
342 1
 
< 0.1%
338 1
 
< 0.1%
335 1
 
< 0.1%
324 1
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct963
Distinct (%)2.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.67149
Minimum-16.8
Maximum41.1
Zeros332
Zeros (%)0.9%
Negative5222
Negative (%)14.9%
Memory size274.1 KiB
2024-03-08T12:10:12.552855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4
Q13.1
median14.6
Q323.5
95-th percentile30.7
Maximum41.1
Range57.9
Interquartile range (IQR)20.4

Descriptive statistics

Standard deviation11.458418
Coefficient of variation (CV)0.83812507
Kurtosis-1.1703241
Mean13.67149
Median Absolute Deviation (MAD)9.9
Skewness-0.10086633
Sum479103.68
Variance131.29535
MonotonicityNot monotonic
2024-03-08T12:10:12.820071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 401
 
1.1%
0 332
 
0.9%
1 326
 
0.9%
-1 315
 
0.9%
2 302
 
0.9%
-2 240
 
0.7%
-4 212
 
0.6%
4 197
 
0.6%
5 196
 
0.6%
-5 193
 
0.6%
Other values (953) 32330
92.2%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
41.1 1
< 0.1%
40.4 1
< 0.1%
40 1
< 0.1%
39.6 1
< 0.1%
38.8 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38.1 1
< 0.1%
38 2
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct595
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1012.5474
Minimum987.1
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:13.505043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum987.1
5-th percentile997
Q11004
median1012.2
Q31020.9
95-th percentile1029.2
Maximum1042
Range54.9
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation10.266059
Coefficient of variation (CV)0.010138843
Kurtosis-0.9080139
Mean1012.5474
Median Absolute Deviation (MAD)8.4
Skewness0.09963305
Sum35483712
Variance105.39196
MonotonicityNot monotonic
2024-03-08T12:10:13.792037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 305
 
0.9%
1021 258
 
0.7%
1025 254
 
0.7%
1024 253
 
0.7%
1022 244
 
0.7%
1020 240
 
0.7%
1026 230
 
0.7%
1014 223
 
0.6%
1019 222
 
0.6%
1016 221
 
0.6%
Other values (585) 32594
93.0%
ValueCountFrequency (%)
987.1 1
 
< 0.1%
987.5 1
 
< 0.1%
987.7 3
< 0.1%
987.8 3
< 0.1%
987.9 1
 
< 0.1%
988.1 1
 
< 0.1%
988.4 1
 
< 0.1%
988.5 1
 
< 0.1%
988.6 2
< 0.1%
988.8 1
 
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)1.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.4475345
Minimum-35.3
Maximum28.8
Zeros74
Zeros (%)0.2%
Negative15475
Negative (%)44.1%
Memory size274.1 KiB
2024-03-08T12:10:14.042004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-20.2
Q1-8.8
median3
Q315
95-th percentile22.1
Maximum28.8
Range64.1
Interquartile range (IQR)23.8

Descriptive statistics

Standard deviation13.810696
Coefficient of variation (CV)5.6426971
Kurtosis-1.1106335
Mean2.4475345
Median Absolute Deviation (MAD)11.9
Skewness-0.19689612
Sum85771.4
Variance190.73532
MonotonicityNot monotonic
2024-03-08T12:10:14.323324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 142
 
0.4%
16.9 133
 
0.4%
16.4 130
 
0.4%
16.2 130
 
0.4%
17.2 129
 
0.4%
17 129
 
0.4%
17.8 128
 
0.4%
17.1 128
 
0.4%
17.3 126
 
0.4%
17.7 122
 
0.3%
Other values (607) 33747
96.2%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.8 2
< 0.1%
28.7 3
< 0.1%
28.5 2
< 0.1%
28.4 4
< 0.1%
28.3 2
< 0.1%
28.2 3
< 0.1%
28.1 3
< 0.1%
28 1
 
< 0.1%
27.9 1
 
< 0.1%
27.8 4
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct119
Distinct (%)0.3%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.064019518
Minimum0
Maximum46.4
Zeros33673
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:14.584410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46.4
Range46.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78628199
Coefficient of variation (CV)12.28191
Kurtosis1020.5059
Mean0.064019518
Median Absolute Deviation (MAD)0
Skewness27.335814
Sum2243.5
Variance0.61823937
MonotonicityNot monotonic
2024-03-08T12:10:14.885377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.5 74
 
0.2%
0.4 71
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.9 41
 
0.1%
0.8 35
 
0.1%
Other values (109) 471
 
1.3%
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.4 71
 
0.2%
0.5 74
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.8 35
 
0.1%
0.9 41
 
0.1%
ValueCountFrequency (%)
46.4 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
29 1
< 0.1%
27.3 1
< 0.1%
24.1 1
< 0.1%
23.7 1
< 0.1%
21.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing78
Missing (%)0.2%
Memory size274.1 KiB
ENE
3861 
E
3564 
NE
3540 
ESE
2706 
SW
2481 
Other values (11)
18834 

Length

Max length3
Median length2
Mean length2.2486423
Min length1

Characters and Unicode

Total characters78671
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowNW
3rd rowNNW
4th rowN
5th rowNNW

Common Values

ValueCountFrequency (%)
ENE 3861
11.0%
E 3564
10.2%
NE 3540
10.1%
ESE 2706
 
7.7%
SW 2481
 
7.1%
NW 2466
 
7.0%
SSW 1953
 
5.6%
NNE 1928
 
5.5%
SE 1880
 
5.4%
N 1865
 
5.3%
Other values (6) 8742
24.9%

Length

2024-03-08T12:10:15.178003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ene 3861
11.0%
e 3564
10.2%
ne 3540
10.1%
ese 2706
 
7.7%
sw 2481
 
7.1%
nw 2466
 
7.0%
ssw 1953
 
5.6%
nne 1928
 
5.5%
se 1880
 
5.4%
n 1865
 
5.3%
Other values (6) 8742
25.0%

Most occurring characters

ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 78671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

WSPM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.8607846
Minimum0
Maximum10.5
Zeros623
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:10:15.416964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11
median1.5
Q32.4
95-th percentile4.455
Maximum10.5
Range10.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2803683
Coefficient of variation (CV)0.68807978
Kurtosis3.3696482
Mean1.8607846
Median Absolute Deviation (MAD)0.6
Skewness1.566528
Sum65220.5
Variance1.6393429
MonotonicityNot monotonic
2024-03-08T12:10:15.692237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1863
 
5.3%
1.1 1838
 
5.2%
1 1782
 
5.1%
1.3 1713
 
4.9%
0.9 1642
 
4.7%
1.4 1572
 
4.5%
1.5 1427
 
4.1%
0.8 1385
 
3.9%
1.6 1324
 
3.8%
0.7 1269
 
3.6%
Other values (91) 19235
54.9%
ValueCountFrequency (%)
0 623
 
1.8%
0.1 234
 
0.7%
0.2 280
 
0.8%
0.3 244
 
0.7%
0.4 473
 
1.3%
0.5 691
2.0%
0.6 956
2.7%
0.7 1269
3.6%
0.8 1385
3.9%
0.9 1642
4.7%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10.3 1
 
< 0.1%
10.2 1
 
< 0.1%
9.9 2
< 0.1%
9.8 1
 
< 0.1%
9.7 4
< 0.1%
9.6 1
 
< 0.1%
9.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Dongsi
35064 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters210384
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDongsi
2nd rowDongsi
3rd rowDongsi
4th rowDongsi
5th rowDongsi

Common Values

ValueCountFrequency (%)
Dongsi 35064
100.0%

Length

2024-03-08T12:10:15.942152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:10:16.100859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
dongsi 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
D 35064
16.7%
o 35064
16.7%
n 35064
16.7%
g 35064
16.7%
s 35064
16.7%
i 35064
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175320
83.3%
Uppercase Letter 35064
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 35064
20.0%
n 35064
20.0%
g 35064
20.0%
s 35064
20.0%
i 35064
20.0%
Uppercase Letter
ValueCountFrequency (%)
D 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 210384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 35064
16.7%
o 35064
16.7%
n 35064
16.7%
g 35064
16.7%
s 35064
16.7%
i 35064
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 35064
16.7%
o 35064
16.7%
n 35064
16.7%
g 35064
16.7%
s 35064
16.7%
i 35064
16.7%

Interactions

2024-03-08T12:10:02.854472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:28.540076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.203511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.509844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.139850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:38.521857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.178223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.562815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.958696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.276784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.907984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.127536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.328479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:57.557714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.355117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.053135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:28.671396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.371712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.660875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.327946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:38.694171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.323911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.740877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.091069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.431809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.104090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.266038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.471497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:57.697387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.507292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.321586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:28.799758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.539026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.831013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.484315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:38.850736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.495718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.871670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.257490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.644546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.248764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.421810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.626626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:58.086073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.691410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.495575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:28.951594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.692818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.997037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.630478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.006961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.669184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.021213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.425083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.848177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.390811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.647426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.800288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:58.246513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.852895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.633027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.081228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.849510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:34.193053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.761302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.148240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.837396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.213957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.561551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:49.012081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.518470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.791885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.931541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:58.433376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.049408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.787850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.245496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:31.999000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:34.337012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:36.935441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.306983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.983243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.385583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.752189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:49.241267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.684631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:53.944194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.099167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:58.669196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.242808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:03.921993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.410563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.137309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:34.505022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.066171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.467393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.146943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.548439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:46.897434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:49.623647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.830212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.089727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.311980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:58.858000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.415786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.068642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.559379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.301822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:34.698779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.196332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.606224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.347547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.708138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.077840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:49.755747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:51.947937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.231384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.466792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.002846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.574884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.203335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.720428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.439471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:34.883454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.343312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.785318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.494673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.843154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.208585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:49.909029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.090702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.371835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.585931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.158575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.722973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.334431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:29.883185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.569901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.108428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.525158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:39.927371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.641235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:44.988381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.350314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.036265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.270245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.503495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.726296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.356207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:01.885198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.504343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:30.045138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.696266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.292215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.709402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:40.342116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.769017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.132868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.538089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.173561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.414837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.631527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.865706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.523007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:02.029056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.690843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:30.209081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:32.845050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.510959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.849979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:40.520903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:42.909807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.286076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.728679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.306110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.555405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.773354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:56.991277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.676112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:02.175961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:04.858326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:30.362904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.007953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.681436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:37.976375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:40.690321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.063000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.470525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:47.856929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.451377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.717606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:54.923532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:57.130218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:59.836556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:02.311346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:05.046489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:30.554509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.175756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.841878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:38.146191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:40.860903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.244036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.636433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.027570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.583098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.850896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.068240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:57.268945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.053921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:02.523931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:05.215335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:30.787132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:33.337014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:35.999393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:38.338231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:41.014461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:43.398595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:45.810928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:48.146244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:50.733621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:52.985268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:55.187483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:57.422082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:00.191972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:02.696031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:10:16.283230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1610.771-0.059-0.4520.7320.8420.0470.0160.637-0.193-0.459-0.006-0.0430.0700.1220.074
DEWP0.1611.0000.068-0.0890.2580.1480.264-0.7750.178-0.2770.815-0.2090.019-0.0210.2560.1220.148
NO20.7710.0681.000-0.047-0.6540.6360.6770.100-0.0610.538-0.257-0.5740.005-0.1050.1110.1420.059
No-0.059-0.089-0.0471.000-0.115-0.040-0.0500.1670.000-0.259-0.1150.0130.0180.0010.0440.0850.862
O3-0.4520.258-0.654-0.1151.000-0.222-0.240-0.452-0.007-0.2480.6060.463-0.0180.304-0.1940.1650.048
PM100.7320.1480.636-0.040-0.2221.0000.888-0.083-0.0810.566-0.049-0.2790.0300.040-0.0220.0950.066
PM2.50.8420.2640.677-0.050-0.2400.8881.000-0.106-0.0220.596-0.018-0.3650.013-0.002-0.0060.1070.055
PRES0.047-0.7750.1000.167-0.452-0.083-0.1061.000-0.0870.257-0.8410.0000.010-0.037-0.0110.0790.148
RAIN0.0160.178-0.0610.000-0.007-0.081-0.022-0.0871.000-0.1530.039-0.005-0.010-0.0070.0420.0050.010
SO20.637-0.2770.538-0.259-0.2480.5660.5960.257-0.1531.000-0.367-0.1740.011-0.009-0.1620.0650.102
TEMP-0.1930.815-0.257-0.1150.606-0.049-0.018-0.8410.039-0.3671.0000.1300.0180.1460.1260.1100.148
WSPM-0.459-0.209-0.5740.0130.463-0.279-0.3650.000-0.005-0.1740.1301.0000.0030.181-0.1520.1810.042
day-0.0060.0190.0050.018-0.0180.0300.0130.010-0.0100.0110.0180.0031.0000.0000.0100.0310.000
hour-0.043-0.021-0.1050.0010.3040.040-0.002-0.037-0.007-0.0090.1460.1810.0001.0000.0000.1280.000
month0.0700.2560.1110.044-0.194-0.022-0.006-0.0110.042-0.1620.126-0.1520.0100.0001.0000.0850.249
wd0.1220.1220.1420.0850.1650.0950.1070.0790.0050.0650.1100.1810.0310.1280.0851.0000.089
year0.0740.1480.0590.8620.0480.0660.0550.1480.0100.1020.1480.0420.0000.0000.2490.0891.000

Missing values

2024-03-08T12:10:05.434494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:10:05.950628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:10:06.420509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133109.09.03.017.0300.089.0-0.51024.5-21.40.0NNW5.7Dongsi
1220133114.04.03.016.0300.088.0-0.71025.1-22.10.0NW3.9Dongsi
2320133127.07.0NaN17.0300.060.0-1.21025.3-24.60.0NNW5.3Dongsi
3420133133.03.05.018.0NaNNaN-1.41026.2-25.50.0N4.9Dongsi
4520133143.03.07.0NaN200.084.0-1.91027.1-24.50.0NNW3.2Dongsi
5620133154.04.09.025.0300.078.0-2.41027.5-21.30.0NW2.4Dongsi
6720133165.05.010.029.0400.067.0-2.51028.2-20.40.0NW2.2Dongsi
7820133173.06.012.040.0400.052.0-1.41029.5-20.40.0NNW3.0Dongsi
8920133183.06.012.041.0500.054.0-0.31030.4-21.20.0NW4.6Dongsi
91020133193.06.09.031.0400.069.00.41030.5-23.30.0N5.5Dongsi
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228147.08.02.09.0200.093.014.61013.3-15.60.0N3.6Dongsi
35055350562017228158.033.02.08.0200.096.015.41013.0-15.00.0NNW3.3Dongsi
35056350572017228169.027.03.010.0200.094.014.91012.6-15.40.0NW2.1Dongsi
350573505820172281712.044.03.012.0300.093.014.21012.5-14.90.0NW3.1Dongsi
350583505920172281813.047.02.018.0300.086.013.41013.0-15.50.0WNW1.4Dongsi
350593506020172281916.051.03.029.0400.073.012.51013.5-16.20.0NW2.4Dongsi
350603506120172282018.045.03.043.0500.054.011.61013.6-15.10.0WNW0.9Dongsi
350613506220172282123.058.05.061.0700.028.010.81014.2-13.30.0NW1.1Dongsi
350623506320172282223.053.09.075.0900.015.010.51014.4-12.90.0NNW1.2Dongsi
350633506420172282330.071.011.087.01200.04.08.61014.1-15.90.0NNE1.3Dongsi